Biometric sensing devices are increasingly common in computer or network security applications, financial applications, surveillance applications, and system access control applications. Biometric sensing devices detect or image a unique physical or behavioral trait of a person, providing biometric data that can reliably identify the person. For example, a fingerprint includes a unique pattern of ridges and valleys that can be imaged by a fingerprint sensor. The image of the fingerprint, or the unique characteristics of the fingerprint, is compared to previously captured reference data, such as a reference fingerprint image. The identity of the person is obtained or verified when the newly captured fingerprint image matches the reference fingerprint image.
Devices that image fingerprints or other biometric data can be subject to noise and other errors. One type of noise is signal fixed pattern noise, a noise that is proportional to the signal produced by the sensors in the biometric sensing device. The signal fixed pattern noise can include two components, namely noise that is produced by variations in signal measurements between the sensors in a biometric sensing device and noise caused by variations in gain stages connected to the sensors in a biometric sensing device. Signal fixed pattern noise can be several orders of magnitude larger than the feature signal or the signal of interest. Signal fixed pattern noise can reduce the dynamic range of the biometric sensing device and produce imprecise or indefinite images or data.